1. Field of the Invention
The present invention relates to a portable terminal. More particularly, the present invention relates to a step length estimation method and a portable terminal for implementing the method.
2. Description of the Related Art
In recent years, as interest in personal health has increased, apparatuses that measure movement by determining movement distances have been developed. The apparatuses are occasionally mounted to mobile communication terminals carried by users.
Such a measuring apparatus estimates the step length of a user to accurately measure the number of steps of the user and therefore movement distance. The estimation of a step length uses a walking frequency and an acceleration variance which are parameters that reflect the characteristics of the change of a step length according to a step pattern. FIG. 1A is a view illustrating a relation between step lengths and walking frequencies, and FIG. 1B is a view illustrating a relation between step lengths and acceleration variances. Referring to FIGS. 1A and 1B, it can be seen that the step lengths have linear relations with the walking frequencies and the acceleration variances. Therefore, a step length can be represented by a linear combination of the two parameters of walking frequency and acceleration variance as in equation (1) below.Step length=a1WF+a2AV+b  (1)
In equation (1), a1, a2 are weights of a walking frequency and an acceleration variance, b is a constant term, WF is a walking frequency, and AV is an acceleration variance.
Therefore, since a walking frequency and a variance value of an accelerometer output when one step is generated can be calculated, the total movement distance can be calculated as in Equation (2) below by summating step lengths when several steps are generated.
                              Movement          ⁢                                          ⁢          distance                =                              ∑                          i              =              1                        n                    ⁢                                          ⁢                                    (                                                                    a                    1                                    ⁢                  W                  ⁢                                                                          ⁢                  F                                +                                                      a                    2                                    ⁢                  A                  ⁢                                                                          ⁢                  V                                +                b                            )                        i                                              (        2        )            
In equation (2), n is the number of detected steps, and a1, a2, b are weights in a linear combination of a walking frequency and an acceleration variance and are calculated by a linear regression.
Step length estimation parameter coefficients are obtained by modeling the relation between a step length and a walking frequency and the relation between a step length and an acceleration variance. However, in the case in which the modeling is performed by integrating a walking state and a running state of a user without discriminating between the two states, in all of the first order model and the second order model, the step length estimation efficiency is lowered to approximately 84 to 83 percent. This is because the patterns of the walking frequency and the acceleration variance are different in a walking state than in a running state and a considerable error is included in the step length estimation parameter coefficient when the states are integrated and modeled. Therefore, a method for estimating a step length using a different step length estimation parameter coefficient according to a walking state and a running state is necessary.